Implantable medical devices are becoming increasingly versatile and able to perform many different physiological sensing functions that enable a clinician to quickly and accurately assess patient health. Traditionally, an accurate assessment of patient health required the clinician to synthesize often divergent or seemingly unrelated indications of patient health. For example, a diagnosis of congestive heart failure might include not only an assessment and evaluation of cardiac function data, but also an evaluation of other physiological factors like patient fatigue or respiration data.
Typically, a clinician will assess patient health by inquiring how the patient feels or asking about the patient's activities and then make an indirect assessment based on the patient's response and the clinician's observation of the patient's appearance. However, these measures are very subjective and are limited to the time of the patient/clinician interaction and the quality of patient recall or willingness to divulge information. These factors affect the quality of the assessment.
Modern implantable medical devices offer objective data to help the clinician assess patient health. Modern medical devices can sense and analyze physiological factors with improved accuracy and report that sensed and analyzed information to the clinician or the patient. The data or information that a medical device reports in the form of a sensed physiological parameter can be characterized as either derived or non-derived data. Non-derived data can be understood as raw biometric information sensed by the medical device that has not been processed to any meaningful degree. For example, non-derived biometric information may comprise the quantified measurement of a patient's heart rate or blood pressure. In contrast, derived data is biometric information that has been analyzed and perhaps assigned some qualitative or quantitative value. For example, as a medical device senses a patient's cardiac cycle and clinically analyzes that information, the medical device may report that an arrhythmia has occurred as the result of sensing and analyzing a cardiac rhythm outside expected parameters. Other derived sensors may include, the cumulative calories burned by daily activity, a weight loss monitor, a participation in activities monitor, a depression monitor or determining the onset of cancer, all of which may be ascertained by sensing physiological data and analyzing that data by using clinically derived algorithms or other analytical tools.
An example of a sensor component of a medical device is an accelerometer. An accelerometer is essentially a device capable of measuring an object's relative orientation in a gravity field. It can directly sense patient movement (non-derived data) and present that information for analysis and perform as a derived sensor. Such derived information might include whether a patient is fatigued by reason of illness or because of overexertion. Thus, relative activity may correspond to relative patient health. In addition to simply determining whether a patient is ambulatory, a sensitive or finely-tuned accelerometer can also determine a patient's relative position, i.e., whether the patient is sitting, standing, sleeping or distinguish whether the patient is prone because he decided to lie down instead of abruptly falling down. A sensitive accelerometer can also detect fine body movement, like the physical reflexes of a person coughing or sneezing.
Coughing is often more than an indication of a respiratory irritation or condition like asthma or the onset of the common cold, but may also be a common side effect of certain drugs. For example, Angiotensin Converting Enzyme (“ACE”) inhibitors may cause a patient to cough when the patient's dosage is too high. Thus, coughing may be used to titrate the appropriate dose of a drug like an ACE inhibitor.
Implantable medical devices comprising cardio-sensors, i.e., pacemakers, can also monitor and sense a patient's cardiac activity and provide remedial therapy. In addition, such medical devices can sense and measure transthoracic impedance as a means to evaluate patient respiration data.
As a measurement of respiration, modern implantable medical devices often employ a sensor that measures transthoracic impedance. Transthoracic impedance is essentially the measure of a voltage across some known spacing or distance. To measure this voltage, the medical device drives a current from the device to the tip of a lead and voltage is measured from another area proximate to the device and another area proximate to the lead. For example, as a person's heart pumps, the transthoracic impedance changes because the heart is moving relative to the implanted device. Similarly, as a person's lung inflates and deflates as he breathes, the geometry of the current flowing between the device and the tip of the lead changes. In measuring respiration, the spacing or distance is situated in such a way that the distance crosses over either a person's left or right lung. Thus, when the geometry changes, the resistance also changes. In the context of breathing, the periodicity of the resistance also can serve as an indication of the relative depth or shallowness of breathing. In other words, a transthoracic impedance sensor can determine the symmetrical relationship between inhalation and exhalation. The symmetry of inhalation to exhalation can establish a pattern of respiration that may have clinical meaning, like determining asthma, apnea or chronic obstructive pulmonary disease (“COPD”). Within the context of detecting an asthma attack, a symmetrical breathing pattern recognized by a transthoracic impedance monitor may comprise the forced expiratory volume over one second (“FEV1”). Modern medical devices that measure transthoracic impedance can be configured to filter out the cardiac component and other impedance noise and concentrate on measuring the breathing component.
An implantable medical device may also employ a sensor that measures blood glucose levels. In this way, the medical device may predict the need for insulin therapy before the patient or clinician observes acute symptoms of hyperglycemia.
However, the data sensed by modern implantable medical devices is often presented in a form that merely reduces the data to some numerical or relative value that requires the clinician to further analyze the numerical or relative value output to make a meaningful clinical assessment. In addition, current implantable medical devices frequently are not analytically robust enough to provide meaningful diagnostic assessments or predictions of patient health beyond the mere reporting of physiological data. Merely reporting physiological data can be of limited value due to a person's natural ability to initially compensate for nascent changes in health status. Because of such analytical and perceptual limitations, sensing cardiac activity or transthoracic impedance data through a single implantable medical device may only provide the clinician with a useful starting point for further clinical analysis.
Thus, for these and other reasons, there is a need for a Patient Management System comprising an implantable medical device further comprising various physiological sensors that sense and report patient data. The system is further adapted to analyze the sensed data in a manner that yields an accurate assessment or prediction of patient health or relative well-being. In this way, the system can be configured to not only report a relative state of patient health and detect early stage disease progression, but also alert the clinician to patient health degradation before the onset of an acute episode or symptomatic illness.